scholarly journals Concurrent validity of two instruments (the Confusion Assessment Method and the Delirium Rating Scale) in the detection of delirium among older medical inpatients

2005 ◽  
Vol 34 (1) ◽  
pp. 72-75 ◽  
Author(s):  
D. Adamis
Keyword(s):  
Concurrent Validity ◽  
Rating Scale ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Confusion Assessment ◽  
Medical Inpatients

Delirium classification by the diagnostic and statistical manual – a moving target

2015 ◽  
Vol 27 (6) ◽  
pp. 881-882 ◽  
Author(s):  
Karin J. Neufeld
Keyword(s):  
Rating Scale ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Classification Systems ◽  
Diagnostic Systems ◽  
Delirium Assessment ◽  
Diagnostic And Statistical Manual ◽  
Dsm 5 ◽  
Medical Inpatients ◽  
Dsm Iv

The following paper, entitled “A Comparison of Delirium Diagnosis in Elderly Medical Inpatients using the CAM, DRS-R98, DSM-IV and DSM-5 Criteria” by Adamis and colleagues, reports the results of a single delirium assessment of 200 medical inpatients, aged 70 years and older. The aim was to compare the prevalence of delirium using two different diagnostic classification systems (DSM-5 and DSM-IV) and two commonly used research tools (Confusion Assessment Method and the Delirium Rating Scale-Revised ‘98). This editorial focuses on the comparison of the two versions of the DSM. The authors conclude that, while both diagnostic systems identify a core concept of delirium, the DSM-IV criteria are the most inclusive of the four approaches and the DSM-5, the most restrictive, identifying a prevalence of 19.5% and 13%, respectively in this sample. Furthermore, they conclude that these two systems do not appear to detect the same patients: only 14 of 26 (54%) individuals identified as delirious by the more exclusive DSM-5 criteria were also identified as such by DSM-IV.

scholarly journals Validation of the Delirium Diagnostic Tool-Provisional (DDT-Pro) With Medical Inpatients and Comparison With the Confusion Assessment Method Algorithm

2020 ◽  
Vol 32 (3) ◽  
pp. 213-226 ◽  
Author(s):  
José G. Franco ◽  
María V. Ocampo ◽  
Juan D. Velásquez-Tirado ◽  
Daniel R. Zaraza ◽  
Alejandra M. Giraldo ◽  
...  
Keyword(s):  
Diagnostic Tool ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Confusion Assessment ◽  
Medical Inpatients

A prospective cohort study on predictors of mortality of delirium in an emergency observational unit

QJM ◽  
2020 ◽  
Author(s):  
S Pal ◽  
N Sharma ◽  
S M Singh ◽  
S Kumar ◽  
A K Pannu
Keyword(s):  
Intensive Care Unit ◽  
Cohort Study ◽  
Intensive Care ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
Rating Scale ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Rapid Screening ◽  
Confusion Assessment

Summary Background Delirium is often an underdiagnosed and underestimated neuropsychiatric syndrome, especially in low- and middle-income countries. Aim To document the prevalence and clinical profile of delirium and to detect the baseline parameters associated with in-hospital mortality. Design A prospective cohort study conducted between January 2016 to December 2016 at an adult medical emergency observational unit of an academic hospital in north India. Methods Confusion Assessment Method for the intensive care unit was used for screening and diagnosis of delirium. Subtypes of delirium and severity were defined with the Richmond agitation-sedation scale and Delirium Rating Scale-Revised-98 (DRS-R-98). Results Out of 939 screened patients, 312 (33.2%) had delirium, including 73.7% unrecognized cases. The mean age was 49.1 ± 17.3 years (range 17–90), and only 33.3% of the patients were above 60 years. The prevalence of hypoactive, mixed and hyperactive delirium was 39.1, 33.7 and 27.2%, respectively. Usual predisposing factors were alcohol use disorder (57.4%) and hypertension (51.0%), and infections remain the most common precipitating factors (42.0%). In total, 96.1% of patients received midazolam before delirium onset, and physical restraints were used in 73.4%. Mortality was higher in delirium (19.9% vs. 6.4%). The independent predictors of death in delirium were low diastolic blood pressure (P-value = 0.000), Glasgow coma scale score <15 (P = 0.026), high Acute Physiology and Chronic Health Evaluation II score (P = 0.007), high DRS-R-98 severity score (P = 0.000) and hyperactive delirium (P = 0.024). Conclusion Rapid screening with Confusion Assessment Method for the intensive care unit detected a high prevalence of delirium (even in young patients), and it associated with high mortality.

scholarly journals Frequency of delirium and subsyndromal delirium in an adult acute hospital population

2014 ◽  
Vol 205 (6) ◽  
pp. 478-485 ◽  
Author(s):  
D. Meagher ◽  
N. O'Regan ◽  
D. Ryan ◽  
W. Connolly ◽  
E. Boland ◽  
...  
Keyword(s):  
General Hospital ◽  
Rating Scale ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Acute Hospital ◽  
Point Prevalence ◽  
Point Prevalence Study ◽  
Subsyndromal Delirium ◽  
Confusion Assessment ◽  
Dsm Iv

BackgroundThe frequency of full syndromal and subsyndromal delirium is understudied.AimsWe conducted a point prevalence study in a general hospital.MethodPossible delirium identified by testing for inattention was evaluated regarding delirium status (full/subsyndromal delirium) using categorical (Confusion Assessment Method (CAM), DSM-IV) and dimensional (Delirium Rating Scale-Revised-98 (DRS-R98) scores) methods.ResultsIn total 162 of 311 patients (52%) screened positive for inattention. Delirium was diagnosed in 55 patients (17.7%) using DSM-IV, 52 (16.7%) using CAM and 58 (18.6%) using DRS-R98⩾12 with concordance for 38 (12.2%) individuals. Subsyndromal delirium was identified in 24 patients (7.7%) using a DRS-R98 score of 7–11 and 41 (13.2%) using 2/4 CAM criteria. Subsyndromal delirium with inattention (v. without) had greater disturbance of multiple delirium symptoms.ConclusionsThe point prevalence of delirium and subsyndromal delirium was 25%. There was modest concordance between DRS-R98, DSM-IV and CAM delirium diagnoses. Inattention should be central to subsyndromal delirium definitions.

Delir-Diagnostik

2007 ◽  
Vol 20 (2-3) ◽  
pp. 135-139
Author(s):  
B. Dittrich ◽  
G. Gatterer ◽  
T. Frühwald ◽  
U. Sommeregger
Keyword(s):  
Confusion Assessment Method ◽  
Assessment Method ◽  
Psychische Störung ◽  
Confusion Assessment

Zusammenfassung: Das Delir (“akuter Verwirrtheitszustand”) bezeichnet eine psychische Störung, die plötzlich auftritt, durch eine rasche Fluktuation von Bewusstseinslage und Aufmerksamkeitsleistung gekennzeichnet ist und eine organische Ursache hat. Dieses Störungsbild nimmt bei Patienten im höheren Lebensalter deutlich an Häufigkeit zu und verursacht durch verlängerte Krankenhausaufenthalte und ungünstige Krankheitsverläufe erhebliche Kosten im Gesundheitssystem. Daher erscheint eine möglichst frühe Erkennung deliranter Zustandsbilder gerade im Rahmen der Geriatrie von großer Bedeutung. Zu diesem Zweck wurde eine deutsche Version der international weit verbreiteten Confusion Assessment Method entwickelt, die für die Bedürfnisse einer Abteilung für Akutgeriatrie modifiziert wurde. Dargestellt werden die Entwicklung und erste Erfahrungen mit diesem Instrument.

3-Minute Diagnostic Interview for Confusion Assessment Method--Chinese Version

2020 ◽  
Author(s):  
Dong-Liang Mu ◽  
Pan-Pan Ding ◽  
Shu-Zhe Zhou ◽  
Mei-Jing Liu ◽  
Xin-Yu Sun ◽  
...  
Keyword(s):  
Confusion Assessment Method ◽  
Assessment Method ◽  
Diagnostic Interview ◽  
Chinese Version ◽  
Confusion Assessment

scholarly journals A Systematic Review and Pooled Prevalence of Paediatric Delirium in Critically Ill Children

2021 ◽  
Vol 29 (Supplement_1) ◽  
pp. i31-i32
Author(s):  
D Semple ◽  
M M Howlett ◽  
J D Strawbridge ◽  
C V Breatnach ◽  
J C Hayden
Keyword(s):  
Risk Factors ◽  
Critical Care ◽  
Critically Ill ◽  
Observational Studies ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Assessment Tools ◽  
Critically Ill Children ◽  
Confusion Assessment ◽  
Pooled Prevalence

Abstract Introduction Paediatric Delirium (PD) is a neuropsychiatric complication that occurs during the management of children in the critical care environment (Paediatric Intensive Care (PICU) and Neonatal Intensive Care (NICU). Delirium can be classified as hypoactive (decreased responsiveness and withdrawal), hyperactive (agitation and restlessness), and mixed (combined) (1). PD can be assessed using a number of assessment tools. PD has been historically underdiagnosed or misdiagnosed, having many overlapping symptoms with other syndrome such as pain and iatrogenic withdrawal syndrome (2). An appreciation of the extent of PD would help clinicians and policy makers drive interventions to improve recognition, prevention and management of PD in clinical practice. Aim To estimate the pooled prevalence of PD using validated assessment tools, and to identify risk factors including patient-related, critical-care related and pharmacological factors. Methods A systematic search of PubMed, EMBASE and CINAHL databases was undertaken. Eligible articles included observational studies or trials that estimated a prevalence of PD in a NICU/PICU population using a validated PD assessment tool. Validated tools are the paediatric Confusion Assessment Method-ICU (pCAM-ICU), the Cornell Assessment of Pediatric Delirium (CAPD), the PreSchool Confusion Assessment Method for the ICU (psCAM-ICU), pCAM-ICU severity scale (sspCAM-ICU), and the Sophia Observation Withdrawal Symptoms scale Paediatric Delirium scale (SOS-PD) (1). Only full text studies were included. No language restrictions were applied. Two reviewers independently screened records. Data was extracted using a pre-piloted form and independently verified by another reviewer. Quality was assessed using tools from the National Institutes of Health. A pooled prevalence was calculated from the studies that estimated PD prevalence using the most commonly applied tool, the CAPD (1). Results Data from 23 observational studies describing prevalence and risk factors for PD in critically ill children were included (Figure 1). Variability in study design and outcome reporting was found. Study quality was generally good. Using the validated tools prevalence ranged from 10–66% of patients. Hypoactive delirium was the most prevalent sub-class identified. Using the 13 studies that used the CAPD tool, a pooled prevalence of 35% (27%-43% 95%CI) was calculated. Younger ages, particularly less than two years old, sicker patients, particularly those undergoing mechanical and respiratory ventilatory support were more at risk for PD. Restraints, the number of sedative medications, including the cumulative use of benzodiazepines and opioids were identified as risk factors for the development of PD. PD was associated with longer durations of mechanical ventilation, longer stays and increased costs. Data on association with increased mortality risk is limited and conflicting. Conclusion PD affects one third of critical care admissions and is resource intense. Routine assessment in clinical practice may facilitate earlier detection and management strategies. Modifiable risk factors such as the class and number of sedative and analgesic medications used may contribute to the development of PD. Early mobility and lessening use of these medications present strategies to prevent PD occurrence. Longitudinal prospective multi-institutional studies to further investigate the presentations of the different delirium subtypes and modifiable risk factors that potentially contribute to the development of PD, are required. References 1. Semple D (2020) A systematic review and pooled prevalence of PD, including identification of the risk factors for the development of delirium in critically ill children. doi: 10.17605/OSF.IO/5KFZ8 2. Ista E, te Beest H, van Rosmalen J, de Hoog M, Tibboel D, van Beusekom B, et al. Sophia Observation withdrawal Symptoms-Paediatric Delirium scale: A tool for early screening of delirium in the PICU. Australian Critical Care. 2018;31(5):266–73

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